Effects of spatial variability in light use efficiency on satellite-based NPP monitoring

Light use efficiency (LUE) algorithms are a potentially effective approach to monitoring global net primary production (NPP) using satellite-borne sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS). However, these algorithms are applied at relatively coarse spatial resolutions (≥1 km), which may subsume significant heterogeneity in vegetation LUE (ε_n, g MJ^?1) and, hence, introduce error. To examine the effects of spatial heterogeneity on a LUE algorithm, imagery from the Advanced Very High Resolution Radiometer (AVHRR) at ≈1-km resolution was used to implement a LUE approach for NPP estimation over a 25-km² area of corn (Zea mays L.) and soybean (Glycine max Merr.) in central Illinois, USA. Results from several ε_n formulations were compared with a NPP reference surface based on measured NPPs and a high spatial resolution land cover surface derived from Landsat ETM+. Determination of ε_n based on measurements of biomass production and monitoring of absorbed photosynthetically active radiation (APAR) revealed that ε_n of soybean was 68% of that for corn. When a LUE algorithm for estimating NPP was implemented in the study area using the assumption of homogeneous cropland and the ε_n for corn, the estimate for total biomass production was 126% of that from the NPP reference surface. Because of counteracting errors, total biomass production using the soybean ε_n was closer (86%) to that from the NPP reference surface. Retention of high spatial resolution land cover to assign ε_n resulted in a total NPP very similar to the reference NPP because differences in leaf phenology between the crop types were small except early in the growing season. These results suggest several alternative approaches to accounting for land cover heterogeneity in ε_n when implementing LUE algorithms at coarse resolution.